Overview

Dataset statistics

Number of variables19
Number of observations1299
Missing cells355
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory192.9 KiB
Average record size in memory152.1 B

Variable types

CAT9
NUM9
BOOL1

Warnings

property_type has constant value "1299" Constant
country has constant value "1299" Constant
division has constant value "1299" Constant
city has constant value "1299" Constant
year_of_renovation has constant value "1299" Constant
current_zones has a high cardinality: 175 distinct values High cardinality
zone has a high cardinality: 76 distinct values High cardinality
century_zone has a high cardinality: 67 distinct values High cardinality
century_zone is highly correlated with zoneHigh correlation
zone is highly correlated with century_zoneHigh correlation
current_zones has 100 (7.7%) missing values Missing
zone has 100 (7.7%) missing values Missing
century_zone has 155 (11.9%) missing values Missing
df_index has unique values Unique
propertiesid has unique values Unique
price has 21 (1.6%) zeros Zeros
interior_area has 129 (9.9%) zeros Zeros
gros_area has 62 (4.8%) zeros Zeros
bedrooms has 84 (6.5%) zeros Zeros
bathrooms has 77 (5.9%) zeros Zeros
other_rooms has 1068 (82.2%) zeros Zeros
year_of_construction has 1266 (97.5%) zeros Zeros

Reproduction

Analysis started2021-05-25 17:06:10.481583
Analysis finished2021-05-25 17:06:23.436117
Duration12.95 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct1299
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean661.7528868
Minimum0
Maximum1321
Zeros1
Zeros (%)0.1%
Memory size10.1 KiB
2021-05-25T19:06:23.553854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile65.9
Q1329.5
median662
Q3995.5
95-th percentile1256.1
Maximum1321
Range1321
Interquartile range (IQR)666

Descriptive statistics

Standard deviation383.0182217
Coefficient of variation (CV)0.5787934277
Kurtosis-1.207526282
Mean661.7528868
Median Absolute Deviation (MAD)333
Skewness-0.002293214572
Sum859617
Variance146702.9581
MonotocityStrictly increasing
2021-05-25T19:06:23.708123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
132110.1%
 
45210.1%
 
43310.1%
 
43410.1%
 
43510.1%
 
43610.1%
 
43710.1%
 
43810.1%
 
43910.1%
 
44010.1%
 
Other values (1289)128999.2%
 
ValueCountFrequency (%) 
010.1%
 
110.1%
 
210.1%
 
310.1%
 
410.1%
 
ValueCountFrequency (%) 
132110.1%
 
132010.1%
 
131910.1%
 
131810.1%
 
131710.1%
 

propertiesid
Real number (ℝ≥0)

UNIQUE

Distinct1299
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6519.856043
Minimum2645
Maximum17239
Zeros0
Zeros (%)0.0%
Memory size10.1 KiB
2021-05-25T19:06:23.855691image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2645
5-th percentile2825.8
Q13487
median4499
Q39615
95-th percentile16135.2
Maximum17239
Range14594
Interquartile range (IQR)6128

Descriptive statistics

Standard deviation4214.956015
Coefficient of variation (CV)0.6464799202
Kurtosis0.2293260881
Mean6519.856043
Median Absolute Deviation (MAD)1288
Skewness1.209140774
Sum8469293
Variance17765854.2
MonotocityStrictly decreasing
2021-05-25T19:06:24.010185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
409510.1%
 
1091510.1%
 
474510.1%
 
269810.1%
 
294110.1%
 
1498910.1%
 
280910.1%
 
293310.1%
 
475410.1%
 
271110.1%
 
Other values (1289)128999.2%
 
ValueCountFrequency (%) 
264510.1%
 
264810.1%
 
264910.1%
 
265010.1%
 
265110.1%
 
ValueCountFrequency (%) 
1723910.1%
 
1722410.1%
 
1721510.1%
 
1720610.1%
 
1720410.1%
 

property_type
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Apartment
1299 
ValueCountFrequency (%) 
Apartment1299100.0%
 
2021-05-25T19:06:24.289319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:06:24.363154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:24.434499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length9
Min length9

property_status
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Used
754 
New
431 
Under Construction
 
62
Under construction
 
46
Not Applicable
 
5
ValueCountFrequency (%) 
Used75458.0%
 
New43133.2%
 
Under Construction624.8%
 
Under construction463.5%
 
Not Applicable50.4%
 
For refurbishment10.1%
 
2021-05-25T19:06:24.554905image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.1%
2021-05-25T19:06:24.648056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:24.773203image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length4
Mean length4.880677444
Min length3

availability
Categorical

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Withdrawn
765 
Available
392 
Sold
133 
Reserved
 
6
Rented
 
1
Other values (2)
 
2
ValueCountFrequency (%) 
Withdrawn76558.9%
 
Available39230.2%
 
Sold13310.2%
 
Reserved60.5%
 
Rented10.1%
 
Potential10.1%
 
In negotiation10.1%
 
2021-05-25T19:06:24.903445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.2%
2021-05-25T19:06:24.996860image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:25.122581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length9
Mean length8.484988453
Min length4

country
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Albania
1299 
ValueCountFrequency (%) 
Albania1299100.0%
 
2021-05-25T19:06:25.632513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:06:25.703414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:25.779296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

division
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Tirana
1299 
ValueCountFrequency (%) 
Tirana1299100.0%
 
2021-05-25T19:06:25.932464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:06:26.050097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:26.119904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

city
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
Tirana
1299 
ValueCountFrequency (%) 
Tirana1299100.0%
 
2021-05-25T19:06:26.272873image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-25T19:06:26.390816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:26.459922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

current_zones
Categorical

HIGH CARDINALITY
MISSING

Distinct175
Distinct (%)14.6%
Missing100
Missing (%)7.7%
Memory size10.1 KiB
Don Bosko
102 
Rruga e Kavajes
 
63
21 dhjetori
 
55
Komuna e Parisit
 
50
Ali Demi
 
47
Other values (170)
882 
ValueCountFrequency (%) 
Don Bosko1027.9%
 
Rruga e Kavajes634.8%
 
21 dhjetori554.2%
 
Komuna e Parisit503.8%
 
Ali Demi473.6%
 
Fresku433.3%
 
Kodra e Diellit403.1%
 
Liqeni i Thate342.6%
 
Astiri322.5%
 
Fusha e Aviacionit282.2%
 
Other values (165)70554.3%
 
(Missing)1007.7%
 
2021-05-25T19:06:26.648275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique90 ?
Unique (%)7.5%
2021-05-25T19:06:26.851659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length90
Median length11
Mean length13.852194
Min length3

zone
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct76
Distinct (%)6.3%
Missing100
Missing (%)7.7%
Memory size10.1 KiB
Don Bosko
108 
21 dhjetori
 
68
Rruga e Kavajes
 
67
Komuna e Parisit
 
54
Ali Demi
 
53
Other values (71)
849 
ValueCountFrequency (%) 
Don Bosko1088.3%
 
21 dhjetori685.2%
 
Rruga e Kavajes675.2%
 
Komuna e Parisit544.2%
 
Ali Demi534.1%
 
Laprake463.5%
 
Fresku433.3%
 
Kodra e Diellit423.2%
 
Astiri423.2%
 
Unaza e re413.2%
 
Other values (66)63548.9%
 
(Missing)1007.7%
 
2021-05-25T19:06:27.054371image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique10 ?
Unique (%)0.8%
2021-05-25T19:06:27.238694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length10
Mean length10.85450346
Min length3

century_zone
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct67
Distinct (%)5.9%
Missing155
Missing (%)11.9%
Memory size10.1 KiB
Don Bosco
108 
21 Dhjetori
 
68
Rruga e Kavajes
 
67
Komuna e Parisit
 
54
Ali Demi
 
53
Other values (62)
794 
ValueCountFrequency (%) 
Don Bosco1088.3%
 
21 Dhjetori685.2%
 
Rruga e Kavajes675.2%
 
Komuna e Parisit544.2%
 
Ali Demi534.1%
 
Laprakë463.5%
 
Fresku433.3%
 
Unaza e Re423.2%
 
Astiri423.2%
 
Liqeni i Thatë342.6%
 
Other values (57)58745.2%
 
(Missing)15511.9%
 
2021-05-25T19:06:27.432169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique6 ?
Unique (%)0.5%
2021-05-25T19:06:27.575618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length9
Mean length10.74210931
Min length3

price
Real number (ℝ≥0)

ZEROS

Distinct383
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93793.2679
Minimum0
Maximum1100000
Zeros21
Zeros (%)1.6%
Memory size10.1 KiB
2021-05-25T19:06:27.707368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42000
Q160000
median80000
Q3110000
95-th percentile192300
Maximum1100000
Range1100000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation60728.80667
Coefficient of variation (CV)0.6474751124
Kurtosis62.94105857
Mean93793.2679
Median Absolute Deviation (MAD)23000
Skewness5.142901065
Sum121837455
Variance3687987960
MonotocityNot monotonic
2021-05-25T19:06:27.855351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
85000393.0%
 
55000332.5%
 
75000332.5%
 
65000302.3%
 
80000272.1%
 
60000272.1%
 
95000262.0%
 
105000221.7%
 
0211.6%
 
70000191.5%
 
Other values (373)102278.7%
 
ValueCountFrequency (%) 
0211.6%
 
10210.1%
 
120020.2%
 
150010.1%
 
600010.1%
 
ValueCountFrequency (%) 
110000010.1%
 
53000010.1%
 
42000010.1%
 
38000010.1%
 
37900010.1%
 

interior_area
Real number (ℝ≥0)

ZEROS

Distinct147
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.22324865
Minimum0
Maximum775
Zeros129
Zeros (%)9.9%
Memory size10.1 KiB
2021-05-25T19:06:28.009123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q164
median88
Q3106
95-th percentile140
Maximum775
Range775
Interquartile range (IQR)42

Descriptive statistics

Standard deviation43.99840011
Coefficient of variation (CV)0.522402078
Kurtosis47.31948931
Mean84.22324865
Median Absolute Deviation (MAD)20
Skewness2.936388499
Sum109406
Variance1935.859213
MonotocityNot monotonic
2021-05-25T19:06:28.156685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01299.9%
 
90382.9%
 
100342.6%
 
95262.0%
 
92251.9%
 
80221.7%
 
110221.7%
 
94211.6%
 
93201.5%
 
102201.5%
 
Other values (137)94272.5%
 
ValueCountFrequency (%) 
01299.9%
 
2010.1%
 
2820.2%
 
3210.1%
 
3320.2%
 
ValueCountFrequency (%) 
77510.1%
 
29010.1%
 
27010.1%
 
25810.1%
 
24710.1%
 

gros_area
Real number (ℝ≥0)

ZEROS

Distinct150
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.67590454
Minimum0
Maximum393
Zeros62
Zeros (%)4.8%
Memory size10.1 KiB
2021-05-25T19:06:28.310884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q173
median98
Q3114
95-th percentile150
Maximum393
Range393
Interquartile range (IQR)41

Descriptive statistics

Standard deviation38.33298832
Coefficient of variation (CV)0.4048864229
Kurtosis5.617280093
Mean94.67590454
Median Absolute Deviation (MAD)20
Skewness0.6164140847
Sum122984
Variance1469.417994
MonotocityNot monotonic
2021-05-25T19:06:28.463373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0624.8%
 
100534.1%
 
110282.2%
 
120272.1%
 
105272.1%
 
104262.0%
 
90262.0%
 
115251.9%
 
70241.8%
 
65231.8%
 
Other values (140)97875.3%
 
ValueCountFrequency (%) 
0624.8%
 
2010.1%
 
2810.1%
 
3120.2%
 
3320.2%
 
ValueCountFrequency (%) 
39310.1%
 
29810.1%
 
29010.1%
 
27010.1%
 
25030.2%
 

bedrooms
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.82986913
Minimum0
Maximum8
Zeros84
Zeros (%)6.5%
Memory size10.1 KiB
2021-05-25T19:06:28.599279image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8624154802
Coefficient of variation (CV)0.471298994
Kurtosis2.701188702
Mean1.82986913
Median Absolute Deviation (MAD)0
Skewness0.3490100705
Sum2377
Variance0.7437604605
MonotocityNot monotonic
2021-05-25T19:06:28.703328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
270854.5%
 
130123.2%
 
317513.5%
 
0846.5%
 
4231.8%
 
570.5%
 
810.1%
 
ValueCountFrequency (%) 
0846.5%
 
130123.2%
 
270854.5%
 
317513.5%
 
4231.8%
 
ValueCountFrequency (%) 
810.1%
 
570.5%
 
4231.8%
 
317513.5%
 
270854.5%
 

bathrooms
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.344110855
Minimum0
Maximum4
Zeros77
Zeros (%)5.9%
Memory size10.1 KiB
2021-05-25T19:06:28.814714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile2
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6037775588
Coefficient of variation (CV)0.4492022044
Kurtosis-0.05604529003
Mean1.344110855
Median Absolute Deviation (MAD)0
Skewness-0.01546653625
Sum1746
Variance0.3645473406
MonotocityNot monotonic
2021-05-25T19:06:28.917360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
170954.6%
 
250438.8%
 
0775.9%
 
370.5%
 
420.2%
 
ValueCountFrequency (%) 
0775.9%
 
170954.6%
 
250438.8%
 
370.5%
 
420.2%
 
ValueCountFrequency (%) 
420.2%
 
370.5%
 
250438.8%
 
170954.6%
 
0775.9%
 

other_rooms
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3833718245
Minimum0
Maximum6
Zeros1068
Zeros (%)82.2%
Memory size10.1 KiB
2021-05-25T19:06:29.027505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9301824926
Coefficient of variation (CV)2.426319393
Kurtosis6.810895922
Mean0.3833718245
Median Absolute Deviation (MAD)0
Skewness2.620299753
Sum498
Variance0.8652394695
MonotocityNot monotonic
2021-05-25T19:06:29.122995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
0106882.2%
 
2886.8%
 
1675.2%
 
3584.5%
 
4110.8%
 
550.4%
 
620.2%
 
ValueCountFrequency (%) 
0106882.2%
 
1675.2%
 
2886.8%
 
3584.5%
 
4110.8%
 
ValueCountFrequency (%) 
620.2%
 
550.4%
 
4110.8%
 
3584.5%
 
2886.8%
 

year_of_construction
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.21170131
Minimum0
Maximum2022
Zeros1266
Zeros (%)97.5%
Memory size10.1 KiB
2021-05-25T19:06:29.235945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2022
Range2022
Interquartile range (IQR)0

Descriptive statistics

Standard deviation317.3217962
Coefficient of variation (CV)6.196275227
Kurtosis34.52963336
Mean51.21170131
Median Absolute Deviation (MAD)0
Skewness6.039520884
Sum66524
Variance100693.1223
MonotocityNot monotonic
2021-05-25T19:06:29.346555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
0126697.5%
 
2020120.9%
 
202190.7%
 
201420.2%
 
202210.1%
 
201910.1%
 
201310.1%
 
201010.1%
 
200810.1%
 
200610.1%
 
Other values (4)40.3%
 
ValueCountFrequency (%) 
0126697.5%
 
199010.1%
 
199810.1%
 
200010.1%
 
200110.1%
 
ValueCountFrequency (%) 
202210.1%
 
202190.7%
 
2020120.9%
 
201910.1%
 
201420.2%
 

year_of_renovation
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
1299 
ValueCountFrequency (%) 
01299100.0%
 
2021-05-25T19:06:29.433631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Interactions

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2021-05-25T19:06:12.289106image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:12.415742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:12.535447image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:12.663435image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:12.790400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:12.920300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:13.050506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:06:13.315771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:13.448033image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:13.580755image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:13.706668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:13.841798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:13.960546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:14.406896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:14.538290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:14.665634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:14.791382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:14.915630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:15.045316image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:15.163547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:15.289815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:15.418978image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:15.552023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:15.679488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:06:15.950527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:16.088635image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:06:16.756060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:16.883849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:06:17.288067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2021-05-25T19:06:21.914988image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:22.051903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:22.193255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:22.324433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2021-05-25T19:06:29.501971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-25T19:06:29.697922image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-25T19:06:29.893626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-25T19:06:30.093322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-25T19:06:30.275570image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

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2021-05-25T19:06:22.971111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:23.163927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2021-05-25T19:06:23.281622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

df_indexpropertiesidproperty_typeproperty_statusavailabilitycountrydivisioncitycurrent_zoneszonecentury_zonepriceinterior_areagros_areabedroomsbathroomsother_roomsyear_of_constructionyear_of_renovation
0017239ApartmentUsedAvailableAlbaniaTiranaTiranaDon BoskoDon BoskoDon Bosco56000687711000
1117224ApartmentUnder ConstructionAvailableAlbaniaTiranaTiranaLiqeni i ThateLiqeni i ThateLiqeni i Thatë1400009410922000
2217215ApartmentUsedAvailableAlbaniaTiranaTirana9 kateshet9 kateshet9 Katëshet14700011211222000
3317206ApartmentUsedAvailableAlbaniaTiranaTiranaOxhaku |##| XhamllikuOxhakuOxhaku8900009800000
4417204ApartmentUsedAvailableAlbaniaTiranaTirana21 dhjetori21 dhjetori21 Dhjetori55000636311000
5517195ApartmentUsedAvailableAlbaniaTiranaTiranaSelviaSelviaSelvia140000010022000
6617168ApartmentUsedAvailableAlbaniaTiranaTiranaLaprakeLaprakeLaprakë47400606011000
7717166ApartmentNewAvailableAlbaniaTiranaTirana21 dhjetori21 dhjetori21 Dhjetori110000798721000
8817154ApartmentUsedAvailableAlbaniaTiranaTiranaBllokuBllokuBlloku20000014115532000
9917148ApartmentUsedAvailableAlbaniaTiranaTiranaBllokuBllokuBlloku20700012813732020060

Last rows

df_indexpropertiesidproperty_typeproperty_statusavailabilitycountrydivisioncitycurrent_zoneszonecentury_zonepriceinterior_areagros_areabedroomsbathroomsother_roomsyear_of_constructionyear_of_renovation
128913122660ApartmentNewSoldAlbaniaTiranaTiranaUnaza e reUnaza e reUnaza e Re550009611032200
129013132659ApartmentUsedWithdrawnAlbaniaTiranaTiranaLiqeni I Tiranes |##| Liqeni i ThateLiqeni I TiranesLiqeni i Tiranës94000999931200
129113142656ApartmentNewSoldAlbaniaTiranaTiranaDon BoskoDon BoskoDon Bosco9800010811822100
129213152654ApartmentUsedAvailableAlbaniaTiranaTiranaAli DemiAli DemiAli Demi65000120040300
129313162652ApartmentUsedSoldAlbaniaTiranaTiranaDon BoskoDon BoskoDon Bosco55000728031200
129413172651ApartmentUsedSoldAlbaniaTiranaTiranaBlv. Zogu i PareBlv. Zogu i PareBlv. Zogu i Pare74000748032200
129513182650ApartmentUsedSoldAlbaniaTiranaTiranaDon BoskoDon BoskoDon Bosco8400010311132200
129613192649ApartmentUsedSoldAlbaniaTiranaTiranaTirana e ReTirana e ReTirana e Re198000138042300
129713202648ApartmentUsedWithdrawnAlbaniaTiranaTiranaTirana e ReTirana e ReTirana e Re9500010511532200
129813212645ApartmentNewSoldAlbaniaTiranaTiranaKasharKasharKashar2200048011000